What You Will Do

ML & AI Systems

• Design, train, and own the full lifecycle of ML models for payment optimization — routing decisions, authorization rate improvement, cost reduction, and fraud signals — using PyTorch, TensorFlow, or XGBoost.

• Build and operate LLM-powered workflows: LangGraph agent orchestration, RAG pipelines, and vector DB integrations (Pinecone, pgvector, or Weaviate).

• Own the MLOps stack end-to-end: experiment tracking (MLflow / W&B), model registry, feature store, and automated retraining pipelines on AWS SageMaker.

• Monitor model health continuously — drift, distribution shifts, retraining triggers — and define evaluation metrics tied directly to business outcomes.

Platform Engineering & Payments Integration

• Build and maintain inference services in Go and Python integrated into live payment routing — strict latency SLAs (<100 ms), zero silent errors.

• Own AWS infrastructure: ECS/EKS, Terraform IaC, SQS/SNS event streaming, RDS/Aurora, and S3 for model artifacts.

• Design and ship on-premise and hybrid deployment architectures for enterprise clients requiring local data residency, including secure data sync pipelines.

• Apply PCI-DSS standards across all components touching payment data; implement tokenization in ML pipelines; design for PSP-specific behavior (Cybersource, Worldpay, Prosa, Cielo, Pagbank, and others).

• Build and maintain RESTful and gRPC APIs that expose AI platform capabilities to merchants and partners.

Technical Leadership

• Own observability end-to-end: Prometheus/Grafana dashboards, OpenTelemetry tracing, model-specific monitors, and on-call runbooks.

• Set the engineering bar for the team: architecture reviews, code standards, testing strategy (unit, integration, shadow mode), and CI/CD practices.

• Mentor engineers, run design reviews, and translate product vision into executable technical roadmaps with clear timelines and trade-offs.

Technical Skills

Backend / Platform Go (production services) Python (ML + tooling) gRPC & REST APIs Event streaming (SQS/SNS) Distributed systems

Cloud & Infra — AWS ECS / EKS Terraform / IaC SageMaker or Vertex AI RDS/Aurora, S3 Hybrid / on-prem deploy

AI / ML Stack PyTorch or TensorFlow XGBoost / scikit-learn MLflow / W&B Feature stores Model monitoring & drift

LLMs & Agents LangGraph / LangChain RAG + vector DBs Prompt engineering LLM evaluation Structured outputs

Payments Domain PCI-DSS compliance Tokenization patterns PSP integrations Auth rate optimization Routing orchestration

Frontend React / Next.js TypeScript Component systems API integration

Observability Prometheus / Grafana OpenTelemetry Structured logging On-call runbooks

Data SQL (analytical) Airflow / dbt Feature pipelines Data quality & lineage

What We Are Looking For

• 8+ years in software engineering; 3+ at Staff, Principal, or Tech Lead level owning a production platform end-to-end.

• Proven track record shipping ML/AI systems to production: training, serving, monitoring, and retraining — not just prototyping.

• Hands-on LLM experience in production: agents, RAG pipelines, or AI workflow orchestration.

• Payments or fintech background with practical knowledge of PSP behavior, PCI-DSS scope, authorization logic, and routing trade-offs.

• Experience designing and deploying on-premise or hybrid enterprise infrastructure.

• Bachelor's degree in Computer Science, Engineering, or equivalent demonstrated depth.